Degraded Image Analysis: An Invariant Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth Estimation and Image Restoration Using Defocused Stereo Pairs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching with Linear Superposition of Layers
IEEE Transactions on Pattern Analysis and Machine Intelligence
How Far Can We Go with Local Optimization in Real-Time Stereo Matching
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Proximal Algorithms for Multicomponent Image Recovery Problems
Journal of Mathematical Imaging and Vision
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We propose a novel approach for estimating a depth-map from a pair of rectified stereo images degraded by blur and contrast change. At each location in image space, information is encoded with a new class of descriptors that are invariant to convolution with centrally symmetric PSF and to variations in contrast. The descriptors are based on local-phase quantization, they can be computed very efficiently and encoded in a limited number of bits. A simple measure for comparing two encoded templates is also introduced. Results show that, the proposed method can represent a cheap but still effective way for estimating disparity maps from degraded images, without making restrictive assumptions; these advantages make it attractive for practical applications.