Neural mechanisms for segregation and recovering of intrinsic image features

We present a single-scale architecture for both segregation and recovering of intrinsic image features and brightness perception. Specifically, a given intensity (or grey scale) image is first analyzed for texture (here defined as small-scale even symmetric features), surfaces (small-scale odd symmetric features) and gradients (large-scale even and odd symmetric features). In this way the image is segregated. Subsequently, textures, surfaces and gradients are recovered by corresponding neural circuits. The proposed architecture may serve as a generic building block for a variety of early vision tasks such as, for example, denoising, efficient coding, as well as mid-level tasks that build on the results from the preceding processing stages.