Abstract
Images play a vital role in scientific research and its capturing are different in nature, so that few images are bad in quality because of bad circumstances like bad lightening, undesirable conditions, error in capturing device itself etc. Contrast enhancement plays an important role for better visualization, to get suitable results and for the improvement of quality of image. In this paper, we proposed a technique for the contrast enhancement of both types of images the color images as well as gray scale images. The proposed method calculate two parameters statistics and phase congruency of the image. Furthermore we use optimal histogram method which is RIQMC (reduced reference image quality metric for contrast changes) based and histogram equalization which is non parametric is used to perform the contrast enhancement. We presented two methodologies, purpose of one is the brightness of the image remain preserve and other is used to adaptively increase its brightness. Subjective comparison is performed by considering the improved contrast and absence of useless artifacts. Analyses of the contrast enhancement images are also perform by the quantitative measure. Simulation results on the different images conclude the significance of our proposed model.
Similar content being viewed by others
References
Singh H, Kumar A, Balyan LK, image Singh GK (2019) A novel optimally weighted framework of piecewise gamma corrected fractional order masking for satellite image enhancement. Computers image Electrical Engineering 75:245–261
Li C, Guo C, Ren W, Cong R, Hou J, Kwong S, image Tao D (2019) An underwater image enhancement benchmark dataset and beyond. IEEE Trans Image Process 29:4376–4389
Ren W, Liu S, Ma L, Xu Q, Xu X, Cao X, image Yang MH (2019) Low-light image enhancement via a deep hybrid network. IEEE Trans Image Process 28(9):4364–4375
de Haan K, Rivenson Y, Wu Y, image Ozcan A (2019) Deep-learning-based image reconstruction and enhancement in optical microscopy. Proc IEEE 108(1):30–50
Rundo L, Tangherloni A, Nobile MS, Militello C, Besozzi D, Mauri G, image Cazzaniga P (2019) MedGA: A novel evolutionary method for image enhancement in medical imaging systems. Expert Syst Appl 119:387–399
Yang M, Hu J, Li C, Rohde G, Du Y, image Hu K (2019) An in-depth survey of underwater image enhancement and restoration. IEEE Access 7:123638–123657
Wang Y, Song W, Fortino G, Qi LZ, Zhang W, image Liotta A (2019) An experimental-based review of image enhancement and image restoration methods for underwater imaging. IEEE Access 7:140233–140251
Dhal KG, Ray S, Das A, image Das S (2019) A survey on nature-inspired optimization algorithms and their application in image enhancement domain. Archives of Computational Methods in Engineering 26 (5):1607–1638
Guo Y, Li H, image Zhuang P (2019) Underwater image enhancement using a multiscale dense generative adversarial network. IEEE J Ocean Eng 45 (3):862–870
Gao SB, Zhang M, Zhao Q, Zhang XS, image Li YJ (2019) Underwater image enhancement using adaptive retinal mechanisms. IEEE Trans Image Process 28(11):5580–5595
Sahu S, Singh AK, Ghrera SP, image Elhoseny M (2019) An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE. Optics Image Laser Technol 110:87–98
Ignatov A, image Timofte R (2019) Ntire 2019 challenge on image enhancement: Methods and results. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp 0–0
Kuang X, Sui X, Liu Y, Chen Q, image Gu G (2019) Single infrared image enhancement using a deep convolutional neural network. Neurocomputing 332:119–128
Gómez P, Semmler M, Schützenberger A, Bohr C, image Döllinger M (2019) Low-light image enhancement of high-speed endoscopic videos using a convolutional neural network. Medical Image Biological Engineering Image Computing 57(7):1451–1463
Gómez P, Semmler M, Schützenberger A., Bohr C, image Döllinger M (2019) Low-light image enhancement of high-speed endoscopic videos using a convolutional neural network. Medical Image Biological Engineering Image Computing 57(7):1451–1463
Lu CH (2009) A new contrast enhancement technique by adaptively increasing the value of histogram. IEEE International Workshop on Imaging System 59:407–411
Sengee N (2009) Image contrast enhancement using Bi Histogram equalization. IEEE Transaction on Consumer Electronics 56:2727–2734
Sheet D (2010) Brightness preserving dynamic fuzzy histogram. IEEE Transaction on Consumer Electronics 56:2475–2480
Ooi C (2010) Adaptive contrast enhancement by brightness preserving. IEEE Transaction on Consumer Electronics 56:2543–2552
Yang KF, Zhang XS, image Li YJ (2019) A biological vision inspired framework for image enhancement in poor visibility conditions. IEEE Trans Image Process 29:1493–1506
Liu S, image Zhang Y (2019) Detail-preserving underexposed image enhancement via optimal weighted multi-exposure fusion. IEEE Trans Consum Electron 65(3):303–311
Ancuti CO, Ancuti C, De Vleeschouwer C, image Sbert M (2019) Color channel compensation (3c): A fundamental pre-processing step for image enhancement. IEEE Trans Image Process 29:2653–2665
Trindade AJ, McKinley MJ, Fan C, Leggett CL, Kahn A, image Pleskow DK (2019) Endoscopic surveillance of Barrett’s esophagus using volumetric laser endomicroscopy with artificial intelligence image enhancement. Gastroenterology 157(2):303–305
Wang W, Chen Z, Yuan X, image Wu X (2019) Adaptive image enhancement method for correcting low-illumination images. Inf Sci 496:25–41
Gupta B, image Tiwari M (2019) Color retinal image enhancement using luminosity and quantile based contrast enhancement. Multidim Syst Sign Process 30(4):1829–1837
Jadiya (2013) Independent histogram equalization using optimal threshold for contrast enhancement and brightness preservation. IEEE International Conference on Computer and Communication Technology 56:54–59
Garcia A (2014) Image enhancement using Bi Histogram equalization. IEEE International Conference on Computer and Communication Technology 6:28–34
Chuan S (2014) Modified histogram based on partitioned dynamic range. IEEE International Conference on Consumer Electronics 16:107–108
Zu H (2015) Range limited double threshold multi histogram equalization. Springal Optical Society of Japan 36:246–255
Rehman KR (2015) Enhanced dynamic quadrant histogram equalization plateau limit for image contrast. IEEE International Conference on Digital Information and Communication Technology 326:86–91
Kim W, Lee R, Park M, image Lee SH (2019) Low-light image enhancement based on maximal diffusion values. IEEE Access 7:129150–129163
Wang YF, Liu HM, image Fu ZW (2019) Low-light image enhancement via the absorption light scattering model. IEEE Trans Image Process 28 (11):5679–5690
Wang YF, Liu HM, image Fu ZW (2019) Low-light image enhancement via the absorption light scattering model. IEEE Trans Image Process 28 (11):5679–5690
Qiu T, Wen C, Xie K, Wen FQ, Sheng GQ, image Tang XG (2019) Efficient medical image enhancement based on CNN-FBB model. IET Image Process 13(10):1736–1744
Yan C, Gong B, Wei Y, image Gao Y (2020) Deep multi-view enhancement hashing for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence
Yang J, Liu L, Jiang T, image Fan Y (2003) A modified Gabor filter design method for fingerprint image enhancement. Pattern Recogn Lett 24(12):1805–1817
HaCohen Y, Shechtman E, Goldman DB, image Lischinski D (2011) Non-rigid dense correspondence with applications for image enhancement. ACM Transactions on Graphics (TOG) 30(4):1–10
Lecca M, Torresani A., image Remondino F (2020) Comprehensive evaluation of image enhancement for unsupervised image description and matching. IET Image Processing
Liao X, Yin J, Chen M, Qin Z (2020) Adaptive payload distribution in multiple images steganography based on image texture features. IEEE Transactions on Dependable and Secure Computing
Liao X, Yu Y, Li B, Li Z, Qin Z (2019) A new payload partition strategy in color image steganography. IEEE Trans Circuits Syst Video Technol 30 (3):685–696
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ghous, M., Khan, A. Efficient image enhancement using improved RIQMC based ROHIM model. Multimed Tools Appl 81, 28823–28847 (2022). https://doi.org/10.1007/s11042-022-12721-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-12721-6