Park Hansen (detailact10)

The effectiveness of the proposed L3Fnet is supported by both visual and numerical comparisons on this dataset. To further analyze the performance of low-light restoration methods, we also propose the L3F-wild dataset that contains LF captured late at night with almost zero lux values. No ground truth is available in this dataset. To perform well on the L3F-wild dataset, any method must adapt to the light level of the captured scene. To do this we use a pre-processing block that makes L3Fnet robust to various degrees of low-light conditions. Lastly, we show that L3Fnet can also be used for low-light enhancement of single-frame images, despite it being engineered for LF data. We do so by converting the single-frame DSLR image into a form suitable to L3Fnet, which we call as pseudo-LF. Our code and dataset is available for download at https//mohitlamba94.github.io/L3Fnet/.Scene text recognition, the final step of the scene text reading system, has made impressive progress based on deep neural networks. However, existing recognition methods devote to dealing with the geometrically regular or irregular scene text. They are limited to the semantically arbitrary-orientation scene text. see more Meanwhile, previous scene text recognizers usually learn the single-scale feature representations for various-scale characters, which cannot model effective contexts for different characters. In this paper, we propose a novel scale-adaptive orientation attention network for arbitrary-orientation scene text recognition, which consists of a dynamic log-polar transformer and a sequence recognition network. Specifically, the dynamic log-polar transformer learns the log-polar origin to adaptively convert the arbitrary rotations and scales of scene texts into the shifts in the log-polar space, which is helpful to generate the rotation-aware and scale-aware visual representation. Next, the sequence recognition network is an encoder-decoder model, which incorporates a novel character-level receptive field attention module to encode more valid contexts for various-scale characters. The whole architecture can be trained in an end-to-end manner, only requiring the word image and its corresponding ground-truth text. Extensive experiments on several public datasets have demonstrated the effectiveness and superiority of our proposed method.We consider lossy compression of a broad class of bilevel images that satisfy the smoothness criterion, namely, images in which the black and white regions are separated by smooth or piecewise smooth boundaries, and especially lossy compression of complex bilevel images in this class. We propose a new hierarchical compression approach that extends the previously proposed fixed-grid lossy cutset coding (LCC) technique by adapting the grid size to local image detail. LCC was claimed to have the best rate-distortion performance of any lossy compression technique in the given image class, but cannot take advantage of detail variations across an image. The key advantages of the hierarchical LCC (HLCC) is that, by adapting to local detail, it provides constant quality controlled by a single parameter (distortion threshold), independent of image content, and better overall visual quality and rate-distortion performance, over a wider range of bitrates. We also introduce several other enhancements of LCC that improve reconstruction accuracy and perceptual quality. These include the use of multiple connection bits that provide structural information by specifying which black (or white) runs on the boundary of a block must be connected, a boundary presmoothing step, stricter connectivity constraints, and more elaborate probability estimation for arithmetic coding. We also propose a progressive variation that refines the image reconstruction as more bits are transmitted, with very small additional overhead. Experimental results with a wide variety of, and especially complex, bilevel images in the given class confirm that the pro