In this paper, we relate the operation of image dynamic range adjustment to the following two tasks: 1) for a high dynamic range (HDR) image, its dynamic range will be mapped to the available dynamic range of display devices and 2) for a low dynamic range (LDR) image, its distribution of intensity will be extended to adequately utilize the full dynamic range of display devices. The common goal of both tasks is to preserve or even enhance the details and improve the visibility of scenes when being matched to the available dynamic range of a display device. In this paper, we propose an efficient method for image dynamic range adjustment with three adaptive steps. First, according to the histogram of the luminance map separated from the given RGB image, two suitable Gamma functions are adaptively selected to separately adjust the luminance of the dark and bright components. Second, an adaptive fusion strategy is proposed to combine the two adjusted luminance maps in order to balance the enhancement of the details in different regions. Third, an adaptive luminance-dependent color restoration method is designed to combine the fused luminance map with the original color components to obtain more consistent color saturation between the images before and after dynamic range adjustment. Extensive experiments show that the proposed method can efficiently compress the dynamic range of HDR scenes with good contrast, clear details, and high structural fidelity of the original image appearance. In addition, the proposed method can also obtain promising performance when being used to enhance LDR nighttime images and greatly facilitates the object (car) detection in nighttime traffic scenes.

We define the task of salient structure (SS) detection to unify the saliency-related tasks, such as fixation prediction, salient object detection, and detection of other structures of interest in cluttered environments. To solve such SS detection tasks, a unified framework inspired by the two-pathway-based search strategy of biological vision is proposed in this paper. First, a contour-based spatial prior (CBSP) is extracted based on the layout of edges in the given scene along a fast non-selective pathway, which provides a rough, task-irrelevant, and robust estimation of the locations where the potential SSs are present. Second, another flow of local feature extraction is executed in parallel along the selective pathway. Finally, Bayesian inference is used to auto-weight and integrate the local cues guided by CBSP and to predict the exact locations of SSs. This model is invariant to the size and features of objects. The experimental results on six large datasets (three fixation prediction datasets and three salient object datasets) demonstrate that our system achieves competitive performance for SS detection (i.e., both the tasks of fixation prediction and salient object detection) compared with the state-of-the-art methods. In addition, our system also performs well for salient object construction from saliency maps and can be easily extended for salient edge detection.

In this paper, we propose a novel model for the computational color constancy, inspired by the amazing ability of the human vision system (HVS) to perceive the color of objects largely constant as the light source color changes. The proposed model imitates the color processing mechanisms in the specific level of the retina, the first stage of the HVS, from the adaptation emerging in the layers of cone photoreceptors and horizontal cells (HCs) to the color-opponent mechanism and disinhibition effect of the non-classical receptive field in the layer of retinal ganglion cells (RGCs). In particular, HC modulation provides a global color correction with cone-specific lateral gain control, and the following RGCs refine the processing with iterative adaptation until all the three opponent channels reach their stable states (i.e., obtain stable outputs). Instead of explicitly estimating the scene illuminant(s), such as most existing algorithms, our model directly removes the effect of scene illuminant. Evaluations on four commonly used color constancy data sets show that the proposed model produces competitive results in comparison with the state-of-the-art methods for the scenes under either single or multiple illuminants. The results indicate that single opponency, especially the disinhibitory effect emerging in the receptive field's subunit-structured surround of RGCs, plays an important role in removing scene illuminant(s) by inherently distinguishing the spatial structures of surfaces from extensive illuminant(s).

The double-opponent (DO) color-sensitive cells in the primary visual cortex (V1) of the human visual system (HVS) have long been recognized as the physiological basis of color constancy. In this work we propose a new color constancy model by imitating the functional properties of the HVS from the single-opponent (SO) cells in the retina to the DO cells in V1 and the possible neurons in the higher visual cortexes. The idea behind the proposed double-opponency based color constancy (DOCC) model originates from the substantial observation that the color distribution of the responses of DO cells to the color-biased images coincides well with the vector denoting the light source color. Then the illuminant color is easily estimated by pooling the responses of DO cells in separate channels in LMS space with the pooling mechanism of sum or max. Extensive evaluations on three commonly used datasets, including the test with the dataset dependent optimal parameters, as well as the intra- and inter-dataset cross validation, show that our physiologically inspired DOCC model can produce quite competitive results in comparison to the state-of-the-art approaches, but with a relative simple implementation and without requiring fine-tuning of the method for each different dataset.

Brightness and color are two basic visual features integrated by the human visual system (HVS) to gain a better understanding of color natural scenes. Aiming to combine these two cues to maximize the reliability of boundary detection in natural scenes, we propose a new framework based on the color-opponent mechanisms of a certain type of color-sensitive double-opponent (DO) cells in the primary visual cortex (V1) of HVS. This type of DO cells has oriented receptive field with both chromatically and spatially opponent structure. The proposed framework is a feedforward hierarchical model, which has direct counterpart to the color-opponent mechanisms involved in from the retina to V1. In addition, we employ the spatial sparseness constraint (SSC) of neural responses to further suppress the unwanted edges of texture elements. Experimental results show that the DO cells we modeled can flexibly capture both the structured chromatic and achromatic boundaries of salient objects in complex scenes when the cone inputs to DO cells are unbalanced. Meanwhile, the SSC operator further improves the performance by suppressing redundant texture edges. With competitive contour detection accuracy, the proposed model has the additional advantage of quite simple implementation with low computational cost.

To effectively perform visual tasks like detecting contours, the visual system normally needs to integrate multiple visual features. Sufficient physiological studies have revealed that for a large number of neurons in the primary visual cortex (V1) of monkeys and cats, neuronal responses elicited by the stimuli placed within the classical receptive field (CRF) are substantially modulated, normally inhibited, when difference exists between the CRF and its surround, namely, non-CRF, for various local features. The exquisite sensitivity of V1 neurons to the center-surround stimulus configuration is thought to serve important perceptual functions, including contour detection. In this paper, we propose a biologically motivated model to improve the performance of perceptually salient contour detection. The main contribution is the multifeature-based center-surround framework, in which the surround inhibition weights of individual features, including orientation, luminance, and luminance contrast, are combined according to a scale-guided strategy, and the combined weights are then used to modulate the final surround inhibition of the neurons. The performance was compared with that of single-cue-based models and other existing methods (especially other biologically motivated ones). The results show that combining multiple cues can substantially improve the performance of contour detection compared with the models using single cue. In general, luminance and luminance contrast contribute much more than orientation to the specific task of contour extraction, at least in gray-scale natural images.