Improving Color Constancy By Optimizing Correction

Camera needs color constancy algorithms to correct the color bias of the captured images,and the color constancy methods are overall improving from static methods to learning-based methods.Recently,with very simple implementation,regression-based color constancy(CC)methods have obtained very competitive performance by applying a correction matrix to the results of some low level-based CC.However,most regressionbased methods,e.g.,Corrected Moment(CM),apply the same correction matrix to all the test images.Considering that a captured image color is usually determined by various factors(e.g.,illuminant and surface reflectance),it is obviously not reasonable enough to apply a same correction to different test images without considering the intrinsic difference among images.Our first work mathematically analyze the key factors that may influence the performance of regression-based CC,and then we design a principled rule to automatically select the suitable training images to learn an optimal correction matrix for each test image.With this principled strategy,the original regression-based CC(e.g.,CM)is clearly improved to obtain more competitive performance on four generally used benchmark datasets.Although learning-based methods generally perform better than static methods,most of them are still lack of generalization ability,e.g.,learning from one type or one scene condition may not be able to apply to another camera or scene.An common and direct solution is to construct a dataset with all of camera types and scene conditions.Unfortunately,for the extreme scenes like underwater,which influenced by other factors such as noise and blur,it is really difficult to construct a dataset with ground truth illuminant for the learning process of learning-based methods.The problem of underwater CC is thus hard to have an effective solution.Our second work propose an underwater image enhancement model inspired by the morphology and function of the teleost fish retina.We aim to solve the common problems of underwater photography raised by noise,blurring and color bias.Specifically,the unique photoreceptor structure of elephantnose fishes is simulated to realize noise reduction,and the feedback from color-sensitive horizontal cells to cones and a red channel compensation are used to correct the color bias.Besides,the characteristic of center-surround opponency of the bipolar cells and another feedback from amacrine cells to interplexiform cells then to horizontal cells serve for haze removal.The single-opponent ganglion cells are used for color enhancement and color correction.Our model utilizes the global statistics to guide the design of each low-level filter,which realizes the self adaption of the main model parameters.Overall,our model is inspired by the image processing procedure in fish retina,and the experiments on extensive underwater images validate the effectiveness of our model.