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The single-image super-resolution techniques (SISR) have been significantly promoted by deep networks. However, the storage and computation complexities of deep models increase dramatically alongside with the reconstruction performance. This paper proposes a densely connected recursive network (DCRN) to trade off the performance and complexity. We introduce an enhanced dense unit by removing the batch normalization (BN) layers and employing the squeeze-and-excitation (SE) structure. A recursive architecture is also adopted to control the parameters of deep networks. Moreover, a de-convolution based residual learning method is proposed to accelerate the residual feature extraction process. The experimental results validate the efficiency of the proposed approach.

Illumination problem is still a bottleneck of robust face recognition system, which demands extracting illumination invariant features. In this field, existing works only consider the variations caused by lighting direction or magnitude (denoted as homogeneous lighting), but the effect of spectral wavelength is always ignored and thus existing illumination invariant descriptors have its limitation on processing face images under different spectral wavelengths (denoted as heterogeneous lighting). We propose a novel gradient based descriptor, namely Logarithm Gradient Histogram (LGH), which takes the illumination direction, magnitude and the spectral wavelength together into consideration, so that it can handle both homogeneous and heterogeneous lightings. Our proposal contributes in three-folds: (1) we incorporate LMSN-LoG filter to eliminate the lighting effect of each image and extract two illumination invariant components, namely logarithm gradient orientation (LGO) and logarithm gradient magnitude (LGM); (2) we propose an effective post-processing strategy to make our model tolerant to noise and generate a histogram representation to integrate both LGO and LGM; (3) we present solid theoretical analysis on the illumination invariant properties of our proposed descriptors. Extensive experimental results on CMU-PIE, Extended YaleB, FRGC and HFB databases are reported to verify the effectiveness of our proposed model. HighlightsTwo illumination invariant components, namely logarithm gradient orientation (LGO) and logarithm gradient magnitude (LGM), are extracted.An effective post-processing strategy is proposed to integrate both LGO and LGM, generating the logarithm gradient histogram (LGH).Solid theoretical analysis on the illumination invariant properties of the proposed descriptors is presented.Competitive results are reported, both in homogeneous and heterogeneous lighting conditions.