# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 3 Image Pre-Processing and Selection
It receives a low-resolution raw capture from a smartphone and tries to correct the noise and misalignment issues with the raw data. To resolve this, the authors implemented a Pyramid Flow-Guided Deformable Convolution Network to help with the performance of the alignment and todecrease the noise. To improve the algorithm's efficiency even more, they added Swin Transformer blocks and groups to enhance the performance of the burst super-resolution task. Results of this algorithm demonstrated that they excelled over earlier approaches, such as HighRes-net [33] and deep burst super-resolution [27]. An adaptive feature consolidation network (AFCNet) [34] for multi-frame super-resolution serves as another illustration of the use of transformers in combination with burst super-resolution. It extracted multi-scale local-global features using an encoder-decoder transformer, which improved feature alignment. A multi-image super-resolution (MISR) that also integrates transformers is TR-MISR [35]; it was applied to the fusion of low-resolution image features. This solution lessened the limitations of multi-image super-resolution transformers. To maintain high-resolution representations with low-resolution inputs, [PERSON] et al. [36] proposed a method for real image restoration and enhancement using multi-scale residual blocks. Their approach entails extracting semantically richer and more spatially precise features from three parallel fully convolutional streams, which will exchange and aggregate information using an attention-based mechanism called selective kernel feature fusion. The proposed method outperforms other image denoising, enhancement and super-resolution algorithms, producing images with reduced noise, sharp edges, smooth homogeneous regions, improved colour reproduction and a natural and vivid appearance with appropriate contrast. [PERSON] et al. [37] considered combining three algorithms: Radial Brightening according to [38]; Contrast Limited Adaptive Histogram Equalization (CLAHE) [39]; and Retinex [40] for multi and single-scale to find the best optimal image pre-processing techniques for image enhancement. They ran 15 tests to see how each algorithm and the order in which they were combined affected the enhancement task before applying them to Canny Edge detection. According to reported results, CLAHE-based combinations performed better because they improved detection efficiency, while Retinex reduced sharpness and Brightening showed no discernible change. EnlightenGAN [41] is a deep-learning-based technique that uses GANs to produce enhanced images from low-light inputs. According to the research results, it performed better than alternative approaches in subjective and objective metrics. Before running an ImageNet-pretrained ResNet-50 classifier, the authors tested the model for the extremely dark [42] dataset due to the increasing interest in image pre-processing for boosting object classification. The findings indicate that the image enhancement task improved classification by 22.02% to 40.92%. Given that labels contain textual information that may have to be processed, it is also important to know how to make the text on the captured image more readable. Fuelled by the increased interest in safeguarding historical documents, [PERSON] et al. [43] tested pre-processing methods on digitalised recepits. Their analysis included thresholding, morphology, and blurring methods for pre-processing.